It is obvious what predictive modeling algorithms like decision trees, neural networks, linear and logistic regression, and others are used for: building predictive models for classification and/or regression. However, they can be useful for many other tasks in a predictive modeling project. Decision trees are particularly transparent models, which is one reason the are a favorite choice of practitioners when the models are used not only to provide predictions, but to explain patterns of behavior found in the data. Other algorithms, like neural networks, are often considered “black box” algorithms, meaning that they are more difficult to interpret
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